FLEXIBLE RETROFITTING AND OPTIMAL SCHEDULING OF COGENERATION SYSTEM BASED ON IMPROVED MEMETIC ALGORITHM

Wang Liming, Liu Yingming, Pang Xinfu, Wang Xiaodong, Wang Hanbo

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 410-419.

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 410-419. DOI: 10.19912/j.0254-0096.tynxb.2024-0244

FLEXIBLE RETROFITTING AND OPTIMAL SCHEDULING OF COGENERATION SYSTEM BASED ON IMPROVED MEMETIC ALGORITHM

  • Wang Liming1,2, Liu Yingming1, Pang Xinfu2, Wang Xiaodong1, Wang Hanbo1
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Abstract

This paper aims at the problem of wind curtailment and power limitation in the cogeneration system under the “heat-oriented” operation mode in the heating season of the “Three North” regions in China. The system's flexibility is improved by introducing time-of-use electricity price and thermal comfort degree to exploit the potential of demand response and by adding a thermal storage tank and electric boiler to decouple heat and power further. An optimal scheduling method considering demand response and heat-power decoupling components is proposed. The method adopts a multi-objective hierarchical sequencing method to deal with the scheduling objectives, including the quality of the scheduling solution, economy, and wind power consumption capacity. An improved memetic algorithm, which uses adaptive crossover probability and mutation probability and a simulated annealing strategy based on neighborhood exchange, is designed to solve the problem and enhance the algorithm's convergence and optimization performance. The simulation results show that the scheme can effectively improve the economic operation and wind power utilization rate of the cogeneration system, and the effect of heat-power decoupling devices is better than that of demand response.

Key words

wind power consumption / combined heat and power plants / demand-side response / improved memetic algorithm / optimal scheduling

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Wang Liming, Liu Yingming, Pang Xinfu, Wang Xiaodong, Wang Hanbo. FLEXIBLE RETROFITTING AND OPTIMAL SCHEDULING OF COGENERATION SYSTEM BASED ON IMPROVED MEMETIC ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 410-419 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0244

References

[1] 李家珏, 李平, 王刚, 等. 计及弃风消纳的热电联产系统的日前调度模型[J]. 太阳能学报, 2021, 42(9): 295-301.
LI J J, LI P, WANG G, et al.Day-to-day scheduling model for cogeneration system accounting for wind power accommodation[J]. Acta energiae solaris sinica, 2021, 42(9): 295-301.
[2] 卞一帆, 谢丽蓉, 杨建宾, 等. 计及弃风的“电气互补-冷热联供” 模式研究[J]. 太阳能学报, 2022, 43(4): 311-317.
BIAN Y F, XIE L R, YANG J B, et al.Study on mode of “electricity and gas complementation-cooling and heating combined supply” considering wind curtailment[J]. Acta energiae solaris sinica, 2022, 43(4): 311-317.
[3] 成后炉, 蔺红. 提高风电消纳能力的区域电网动态划分[J]. 太阳能学报, 2020, 41(12): 26-32.
CHENG H L, LIN H.Dynamic partition of regional power grids to improve wind power accommodation capacity[J]. Acta energiae solaris sinica, 2020, 41(12): 26-32.
[4] GIMELLI A, MOTTOLA F, MUCCILLO M, et al.Optimal configuration of modular cogeneration plants integrated by a battery energy storage system providing peak shaving service[J]. Applied energy, 2019, 242: 974-993.
[5] HE Y, GUO S, ZHOU J X, et al.The many-objective optimal design of renewable energy cogeneration system[J]. Energy, 2021, 234: 121244.
[6] LI P, HU Q Y, SUN Y, et al.Thermodynamic and economic performance analysis of heat and power cogeneration system based on advanced adiabatic compressed air energy storage coupled with solar auxiliary heat[J]. Journal of energy storage, 2021, 42: 103089.
[7] DENG B F, FANG J K, HUI Q, et al.Optimal scheduling for combined district heating and power systems using subsidy strategies[J]. CSEE journal of power and energy systems, 2019, 5(3): 399-408.
[8] KHATIBI M, BENDTSEN J D, STOUSTRUP J, et al.Exploiting power-to-heat assets in district heating networks to regulate electric power network[J]. IEEE transactions on smart grid, 2021, 12(3): 2048-2059.
[9] XU X D, MING W L, ZHOU Y, et al.Unlock the flexibility of combined heat and power for frequency response by coordinative control with batteries[J]. IEEE transactions on industrial informatics, 2021, 17(5): 3209-3219.
[10] DOU C X, ZHOU X H, ZHANG T F, et al.Economic optimization dispatching strategy of microgrid for promoting photoelectric consumption considering cogeneration and demand response[J]. Journal of modern power systems and clean energy, 2020, 8(3): 557-563.
[11] ALABI T M, LU L, YANG Z Y.Data-driven optimal scheduling of multi-energy system virtual power plant (MEVPP) incorporating carbon capture system (CCS), electric vehicle flexibility, and clean energy marketer (CEM) strategy[J]. Applied energy, 2022, 314: 118997.
[12] LIU C R, WANG H Q, WANG Z Y, et al.Research on life cycle low carbon optimization method of multi-energy complementary distributed energy system: a review[J]. Journal of cleaner production, 2022, 336: 130380.
[13] ZHOU Y F, HU W, MIN Y, et al.Integrated power and heat dispatch considering available reserve of combined heat and power units[J]. IEEE transactions on sustainable energy, 2019, 10(3): 1300-1310.
[14] GARMABDARI R, MOGHIMI M, YANG F, et al.Multi-objective optimisation and planning of grid-connected cogeneration systems in presence of grid power fluctuations and energy storage dynamics[J]. Energy, 2020, 212: 118589.
[15] XU D, WU Q W, ZHOU B, et al.Distributed multi-energy operation of coupled electricity, heating, and natural gas networks[J]. IEEE transactions on sustainable energy, 2020, 11(4): 2457-2469.
[16] MORADI-DALVAND M, NAZARI-HERIS M, MOHAMMADI-IVATLOO B, et al.A two-stage mathematical programming approach for the solution of combined heat and power economic dispatch[J]. IEEE systems journal, 2020, 14(2): 2873-2881.
[17] RIGO-MARIANI R, ZHANG C, ROMAGNOLI A, et al.A combined cycle gas turbine model for heat and power dispatch subject to grid constraints[J]. IEEE transactions on sustainable energy, 2020, 11(1): 448-456.
[18] DOLATABADI A, JADIDBONAB M, MOHAMMADI-IVATLOO B.Short-term scheduling strategy for wind-based energy hub: a hybrid stochastic/IGDT approach[J]. IEEE transactions on sustainable energy, 2019, 10(1): 438-448.
[19] HE Y X, LYU Y, CHE Y R.Operational optimization of combined cooling, heat and power system based on information gap decision theory method considering probability distribution[J]. Sustainable energy technologies and assessments, 2022, 51: 101977.
[20] DOU C X, ZHOU X H, ZHANG T F, et al.Economic optimization dispatching strategy of microgrid for promoting photoelectric consumption considering cogeneration and demand response[J]. Journal of modern power systems and clean energy, 2020, 8(3): 557-563.
[21] LIU G, QIN Z F, DIAO T Y, et al.Low carbon economic dispatch of biogas-wind-solar renewable energy system based on robust stochastic optimization[J]. International journal of electrical power & energy systems, 2022, 139: 108069.
[22] JIANG Y B, WAN C, BOTTERUD A, et al.Efficient robust scheduling of integrated electricity and heat systems: a direct constraint tightening approach[J]. IEEE transactions on smart grid, 2021, 12(4): 3016-3029.
[23] SHAHEEN A M, GINIDI A R, EL-SEHIEMY R A, et al. Optimal economic power and heat dispatch in Cogeneration Systems including wind power[J]. Energy, 2021, 225: 120263.
[24] ZHANG G H, MA X J, WANG L, et al.Elite archive-assisted adaptive memetic algorithm for a realistic hybrid differentiation flowshop scheduling problem[J]. IEEE transactions on evolutionary computation, 2022, 26(1): 100-114.
[25] WANG J J, WANG L.A bi-population cooperative memetic algorithm for distributed hybrid flow-shop scheduling[J]. IEEE transactions on emerging topics in computational intelligence, 2021, 5(6): 947-961.
[26] ZHAO Z Y, LIU S X, ZHOU M C, et al.Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem[J]. IEEE/CAA journal of automatica sinica, 2021, 8(6): 1199-1209.
[27] GE Y F, YU W J, CAO J L, et al.Distributed memetic algorithm for outsourced database fragmentation[J]. IEEE transactions on cybernetics, 2021, 51(10): 4808-4821.
[28] GUAN B X, ZHAO Y H, YIN Y, et al.Detecting disease-associated SNP-SNP interactions using progressive screening memetic algorithm[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2022, 19(2): 878-887.
[29] LI Z C, TANG L X, LIU J Y.A memetic algorithm based on probability learning for solving the multidimensional knapsack problem[J]. IEEE transactions on cybernetics, 2022, 52(4): 2284-2299.
[30] HUANG T, GONG Y J, KWONG S, et al.A niching memetic algorithm for multi-solution traveling salesman problem[J]. IEEE transactions on evolutionary computation, 2020, 24(3): 508-522.
[31] 蔡国伟, 西禹霏, 杨德友. 基于SCA分解法的含电热泵和储热装置的热电联调[J]. 太阳能学报, 2019, 40(12): 3401-3408.
CAI G W, XI Y F, YANG D Y.Decomposition solution for combined heat and power disatch cotaining EHP and TES through SCA algorithm[J]. Acta energiae solaris sinica, 2019, 40(12): 3401-3408.
[32] WANG D S, TAN D P, LIU L.Particle swarm optimization algorithm: an overview[J]. Soft computing, 2018, 22(2): 387-408.
[33] PANG X F, ZHANG X, LIU W, et al.Optimal scheduling of cogeneration system with heat storage device based on artificial bee colony algorithm[J]. Electronics, 2022, 11(11): 1725.
[34] YU H, LI J Q, CHEN X L, et al.An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times[J]. Cluster computing, 2022, 25(5): 3591-3616.
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